Engineering topics

Mcnab, Alan L.

Publications and source records attributed to Mcnab, Alan L..

6 records

NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗

NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban bias

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data -- the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data -- to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km x 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1 deg x 1 deg latitude-longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40 C for monthly minimum temperatures, near 0.25 C for monthly mean temperatures, and near 0.10 C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near -1.1 C for rural stations to 2.4 C for stations from the largest urban areas. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.

Epperson, David L.↗

NASA NTRS1994

Estimating urban temperature bias using polar-orbiting satellite data

Urban temperature bias, defined to be the difference between a shelter temperature reading of unknown but suspected urban influence and some appropriate rural reference temperature, is estimated through the use of polar-orbiting satellite data. Predicted rural temperatures, based on a method developed using sounding data, are shown to be of reasonable accuracy in many cases for urban bias assessments using minimum temperature data from selected urban regions in the United States in July 1989. Assessments of predicted urban bias were based on comparisons with observed bias, as well as independent measures of urban heat island influence, such as population statistics and urban-rural differences in a vegetation index. This technique provides a means of determining urban bias in regions where few if any rural reference stations are available, or where inhomogeneities exist in land surface characteristics or rural station locations.

Johnson, Gregory L.↗

NASA NTRS1993

The use of polar-orbiting satellite sounding data to estimate rural maximum and minimum temperatures

Atmospheric sounding products from NOAA's polar-orbiting satellites were used to derive and test predictive equations of rural shelter-level maximum and minimum temperatures. Sounding data from both winter and summer months were combined with surface data from over 5300 cooperative weather stations in the continental United States to develop multiple linear regression equations. Separate equations were developed for both maximum and minimum temperature, using the three types of sounding retrievals (clear, partly cloudy, and cloudy). Clear retrieval models outperformed others, and maximum temperatures were more accurately predicted than minimums. Average standard deviations of observed rural shelter temperatures within sounding search areas were of similar magnitude to root-mean-square errors from satellite estimates for most clear and partly cloudy cases, but were significantly less for cloudy retrieval cases. Model validation for surrogate polar and tropical climatic regions showed success in application of the four clear retrieval models (maximum and minimum temperature, for both winter and summer). This indicates the potential adaptability of these models to estimates of rural shelter temperature in areas outside of the United States.

Johnson, Gregory L.↗

NASA NTRS1993

The use of a satellite derived vegetation index for assessment of the urban heat island effect

Satellite derived normalized difference (ND) vegetation index data, based on urban and rural region composed of a variety of land surface environments, are evaluated. These data are linearly related to the difference in observed urban and rural minimum temperatures. It is concluded that the difference in the ND index between urban and rural regions reflects the difference in the surface properties (evaporation and heat storage capacity) of these two environments and urban and rural minimum temperatures (the urban heat island effect).

Gallo, Kevin P.↗

NASA NTRS1990

A preliminary analysis of the covariation of surface and midtropospheric temperatures

Monthly, seasonal, and annual averages of surface temperatures and midtropospheric (850-300 hPa) layer-mean temperatures for a global network of 100 stations are analyzed for the periods 1950-87 and 1979-88. The periods were selected on the basis of available data products. A preliminary analysis of the covariation between surface temperatures and midtroposphere layer-mean temperatures indicates a strong correlation in the winter hemispheres over midlatitudes and a much weaker correlation in most of the tropics. Results are compared with other in-situ data sets and with satellite Microwave Sounding Unit (MSU) data.

Elms, Joe D.↗